Download the maize B73 V4 data, and the genome assembly released by https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02029-9
wget ftp://ftp.ensemblgenomes.org/pub/plants/release-34/gff3/zea_mays/Zea_mays.AGPv4.34.gff3.gz
gunzip Zea_mays.AGPv4.34.gff3.gz
wget ftp://ftp.ensemblgenomes.org/pub/plants/release-34/fasta/zea_mays/dna/Zea_mays.AGPv4.dna.toplevel.fa.gz
gunzip Zea_mays.AGPv4.dna.toplevel.fa.gz
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/902/714/155/GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b/GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna.gz
gunzip GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna.gzrename the GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna entry names, to be consistent with the B73 genome fasta file
sed -i 's/, whole genome shotgun sequence//' GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna
sed -i 's/>.*Zea mays genome assembly, chromosome: />/' GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fnausing proali to extract protein coding sequence.
NOTE: please do NOT use CDS extracted using other software. Since proali filtered some CDS records to minimum the impact of minimap2 limitation on genome alignment that “Minimap2 often misses small exons” (https://github.com/lh3/minimap2#limitations)
anchorwave gff2seq -r Zea_mays.AGPv4.dna.toplevel.fa -i Zea_mays.AGPv4.34.gff3 -o cds.fause minimap2 (https://github.com/lh3/minimap2) to map the extracted sequence to the reference genome sequence and query genome
minimap2 -x splice -t 11 -k 12 -a -p 0.4 -N 20 GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna cds.fa > cds.sam
minimap2 -x splice -t 11 -k 12 -a -p 0.4 -N 20 Zea_mays.AGPv4.dna.toplevel.fa cds.fa > ref.samperform anchros alignment using two approach implemented in AnchorWave
anchorwave genoAli -i Zea_mays.AGPv4.34.gff3 -r Zea_mays.AGPv4.dna.toplevel.fa -as cds.fa -a cds.sam -ar ref.sam -s GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna -n anchorsiv -IV -ns
anchorwave proali -i Zea_mays.AGPv4.34.gff3 -r Zea_mays.AGPv4.dna.toplevel.fa -as cds.fa -a cds.sam -ar ref.sam -s GCA_902714155.1_Zm-B73_AB10-REFERENCE-NAM-1.0b_genomic.fna -n anchorspro -ns -R 1 -Q 1 -ns# here I am using the Cairo library to compile the output plot. The output file looks better than native library, but it a little bit of mass up the Rmarkdown output file.
library(ggplot2)
library(compiler)
enableJIT(3)## [1] 3library(ggplot2)
library("Cairo")
changetoM <- function ( position ){
position=position/1000000;
paste(position, "M", sep="")
}
data =read.table("anchorspro", head=TRUE)
data = data[which(data$refChr %in% c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" )),]
data = data[which(data$queryChr %in% c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" )),]
data$refChr = factor(data$refChr, levels=c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ))
data$queryChr = factor(data$queryChr, levels=c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ))
data$strand = factor(data$strand)
data = data[which(data$gene != "intergenetic"),]
p = ggplot(data=data, aes(x=queryStart, y=referenceStart))+geom_point(size=2, aes(color=factor(strand)))+facet_grid(refChr~queryChr, scales="free", space="free" )+ theme_grey(base_size = 60) +
labs(x="B73-Ab10", y="B73")+
theme(axis.line = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
axis.text.y = element_text( colour = "black"),
legend.position='none',
axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black") )
CairoPNG(file="anchorspro.png",width = 4800, height = 3200)
p
dev.off()## png
## 2CairoPDF(file="anchorspro.pdf",width = 60, height = 40)
p
dev.off()## png
## 2The above plot suggested there is no unterchromosomal translocations happened
data =read.table("anchorsiv", head=TRUE)
data = data[which(data$refChr %in% c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" )),]
data = data[which(data$queryChr %in% c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" )),]
data$refChr = factor(data$refChr, levels=c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ))
data$queryChr = factor(data$queryChr, levels=c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10" ))
data$strand = factor(data$strand)
data = data[which(data$gene != "intergenetic"),]
p = ggplot(data=data, aes(x=queryStart, y=referenceStart))+geom_point(size=2, aes(color=factor(strand)))+facet_grid(refChr~queryChr, scales="free", space="free" )+ theme_grey(base_size = 60) +
labs(x="B73-Ab10", y="B73")+ scale_x_continuous(labels=changetoM) + scale_y_continuous(labels=changetoM) +
theme(axis.line = element_blank(),
panel.background = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
axis.text.y = element_text( colour = "black"),
legend.position='none',
axis.text.x = element_text(angle=300, hjust=0, vjust=1, colour = "black") )
CairoPNG(file="anchorsiv.png",width = 4800, height = 3200)
p
dev.off()## png
## 2CairoPDF(file="anchorsiv.pdf",width = 60, height = 40)
p
dev.off()## png
## 2data =read.table("anchorsiv", head=TRUE)
data = data[which(data$refChr %in% c("10" )),]
data = data[which(data$queryChr %in% c("10" )),]
data0 = data.frame(x1=rep(1, nrow(data)), x2=rep(2, nrow(data)), y1=data$referenceStart, y2=data$queryStart, strand=data$strand )
data = data.frame(y=c("1","1","1","1","2","2","2","2"), x=c(144424469, 141366825, 150831625, 144633000, 153050093, 157236690, 159325876, 167708883), color=c("#F8766D", "#F8766D", "#F8766D", "#F8766D", "#F8766D","#F8766D", "#F8766D", "#F8766D"))
data = data.frame(y=c("1","1","2","2","2"), x=c(144424469, 141366825, 159325876, 167708883, 142302496), color=c("#F8766D", "#F8766D", "#F8766D", "#F8766D", "#00BFC4"))
d0 <- data.frame(x1 = 141182907, x2 = 142302496, y1 = 1, y2 = 2)
d1 <- data.frame(x1 = 144424469, x2 = 153050093, y1 = 1, y2 = 2)
d2 <- data.frame(x1 = 141366825, x2 = 157236690, y1 = 1, y2 = 2)
d3 <- data.frame(x1 = 150831625, x2 = 159325876, y1 = 1, y2 = 2)
d4 <- data.frame(x1 = 144633000, x2 = 167708883, y1 = 1, y2 = 2)
d=data.frame(x=c(144424469, 141366825, 157236690, 153050093), y=c("1", "1", "2", "2"), t=c('a','a','a','a'))
dd=data.frame(x=c(150831625, 144633000, 167708883, 159325876), y=c("1", "1", "2", "2"), t=c('a','a','a','a'))
data0 =data0[which(data0$y1 >= 140000000),]
myguide <- guide_legend(keywidth = unit(2, "cm"), keyheight= unit(0.8, "cm"))
p = ggplot(data=data, aes(x=x,y=y))+
geom_segment(aes(x=140000000, xend=150982314, y=1,yend=1), color = "darkgray", size=2) +
geom_segment(aes(x=140000000, xend=172000000, y=2, yend=2), color = "darkgray", size=2) +
geom_segment(aes(x = y1, y = x1, xend = y2, yend = x2, color = strand), data = data0, size=0.5, alpha=0.4) +
geom_point(color=c("#F8766D", "#F8766D","#F8766D", "#F8766D", "#00BFC4"), size=2) +
annotate("text", x = 146700000, y = 0.675, label = "Zm00001d026368", color = "#F8766D", parse = TRUE, size=6, angle = 315) +
annotate("text", x = 143700000, y = 0.675, label = "Zm00001d026218", color = "#F8766D", parse = TRUE, size=6, angle = 315)+
annotate("text", x = 159325876, y = 2.06, label = "Zm00001d026712", color = "#F8766D", parse = TRUE, size=6)+
annotate("text", x = 167708883, y = 2.06, label = "Zm00001d026376", color = "#F8766D", parse = TRUE, size=6) +
annotate("text", x = 142302496, y = 2.06, label = "Zm00001d026214", color = "#00BFC4", parse = TRUE, size=6) +
theme_grey(base_size = 24) +
scale_colour_manual(name='strand',
values=c('+'='#00BFC4', '-'='#F8766D'), guide = myguide) +
labs(x="", y="")+scale_x_continuous(limits=c(140000000, 172000000), labels=changetoM) +scale_y_discrete(labels=c("1" = "B73", "2" = "B73-Ab10") ) +
theme(axis.line = element_blank(),
panel.background = element_blank(),
panel.border = element_blank(),
legend.key = element_rect(colour = NA, fill = NA),
axis.text.y = element_text( colour = "black"),
legend.position = c(0.8, 0.4),
legend.background = element_rect(fill="transparent"),
axis.text.x = element_text( colour = "black"),
axis.ticks.y=element_blank())
CairoPNG(file="inversionv2.png",width = 960, height = 560)
p